class_docstr: Just checks batch-equality of two tensors and computes an accuracy metric based on that.  This
  is similar to :class:`CategoricalAccuracy`, if you've already done a ``.max()`` on your
  predictions.  If you have categorical output, though, you should typically just use
  :class:`CategoricalAccuracy`.  The reason you might want to use this instead is if you've done
  some kind of constrained inference and don't have a prediction tensor that matches the API of
  :class:`CategoricalAccuracy`, which assumes a final dimension of size ``num_classes``.
docstr: Returns
  -------
  The accumulated accuracy.
docstr_tok: ["Returns","--","--","--","-","The","accumulated","accuracy","."]
code: def get_metric(self, reset=False):
      """
      Returns
      -------
      The accumulated accuracy.
      """
      accuracy = float(self._correct_count) / float(self._total_count)
      if reset:
          self.reset()
      return accuracy
  
file_path: magnitude/pymagnitude/third_party/allennlp/training/metrics/boolean_accuracy.py
imports: from __future__ import division
  from __future__ import absolute_import
  import torch
  from allennlp.training.metrics.metric import Metric
label_desc: Use image node_id 3 for calling the BooleanAccuracy obj's underlying member method code with example usage: obj.get_metric(reset) and returns: accuracy